Pulse onset detection using neighbor pulse-based signal enhancement

Xu, Peng; Bergsneider, Marvin; Hu, Xiao

doi:10.1016/j.medengphy.2008.06.005

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Volume 31, Issue 3 , Pages 337-345, April 2009

Pulse onset detection using neighbor pulse-based signal enhancement

Peng Xu
- Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California, Los Angeles, United States
- Center of Neuroinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
Marvin Bergsneider
- Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California, Los Angeles, United States
- Biomedical Engineering Graduate Program, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, United States
Xiao Hu
- Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California, Los Angeles, United States
- Biomedical Engineering Graduate Program, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, United States
- Corresponding author at: 100 Medical Plaza, Suite 219, Los Angeles, CA 90095, United States. Tel.: +1 310 825 9207; fax: +1 310 206 5234.

Received 6 February 2008; received in revised form 25 May 2008; accepted 1 June 2008. published online 21 July 2008.

Abstract

Detecting onsets of cardiovascular pulse wave signals is an important prerequisite for successfully conducting various analysis tasks involving the concept of pulse wave velocity. However, pulse onsets are frequently influenced by inherent noise and artifacts in signals continuously acquired in a clinical environment. The present work proposed and validated a neighbor pulse-based signal enhancement algorithm for reducing error in the detected pulse onset locations from noise-contaminated pulsatile signals. Pulse onset was proposed to be detected using the first principal component extracted from three adjacent pulses. This algorithm was evaluated using test signals constructed by mixing arterial blood pressure, cerebral blood flow velocity and intracranial pressure pulses recorded from neurosurgical patients with white noise of various levels. The results showed that the proposed pulse enhancement algorithm improved (p<0.05) pulse onset detection according to all three different onset definitions and for all three types of pulsatile signals as compared to results without using the pulse enhancement. These results suggested that the proposed algorithm could help achieve robustness in pulse onset detection and facilitate pulse wave analysis using clinical recordings.